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Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems

机译：将机器学习集成到项目响应理论中解决自适应学习系统中的冷启动问题

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摘要

Adaptive learning systems aim to provide learning items tailored to the behavior and needs of individual learners. However, one of the outstanding challenges in adaptive item selection is that often the corresponding systems do not have information on initial ability levels of new learners entering a learning environment. Thus, the proficiency of those new learners is very difficult to be predicted. This heavily impairs the quality of personalized items' recommendation during the initial phase of the learning environment. In order to handle this issue, known as the cold-start problem, we propose a system that combines item response theory (IRT) with machine learning. Specifically, we perform ability estimation and item response prediction for new learners by integrating IRT with classification and regression trees built on learners' side information. The goal of this work is to build a learning system that incorporates IRT and machine learning into a unified framework. We compare the proposed hybrid model to alternative approaches by conducting experiments on two educational data sets. The obtained results affirmed the potential of the proposed method. In particular, the obtained results indicate that IRT combined with Random Forests provides the lowest error for the ability estimation and the highest accuracy in terms of response prediction. This way, we deduce that the employment of machine learning in combination with IRT could indeed alleviate the effect of the cold start problem in an adaptive learning environment.
机译：自适应学习系统的目标是提供针对个别学习者的行为和需求量身定制的学习项目。然而，自适应项目选择中的一个突出挑战之一是，通常相应的系统通常没有关于进入学习环境的新学习者的初始能力水平的信息。因此，这些新学习者的熟练程度很难预测。在学习环境的初始阶段，这重益损害了个性化物品推荐的质量。为了处理这个问题，称为冷启动问题，我们提出了一个将物品响应理论（IRT）与机器学习结合的系统。具体而言，我们通过将IRT与基于学习者侧面信息的分类和回归树集成来执行新学习者的能力估计和项目响应预测。这项工作的目标是建立一个学习系统，该系统将IRT和机器学习融入统一的框架。我们通过对两个教育数据集进行实验进行比较拟议的混合模型来替代方法。获得的结果肯定了所提出的方法的潜力。特别是，所获得的结果表明，IRT与随机林结合提供了能力估计的最低误差和在响应预测方面的最高精度。这样，我们推断了与IRT组合的机器学习的就业确实可以减轻冷启动问题在自适应学习环境中的影响。

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  • 来源
    《Computers & education》 |2019年第8期|91-103|共13页
  • 作者

    Pliakos Konstantinos; Joo Seang-Hwane; Park Jung Yeon; Cornillie Frederik; Vens Celine; Van den Noortgate Wim;

  • 作者单位

    Katholieke Univ Leuven Dept Publ Hlth & Primary Care Fac Med Campus Kulak Etienne Sabbelaan 53 B-8500 Kortrijk Belgium|Katholieke Univ Leuven ITEC Imec Leuven Belgium;

    Katholieke Univ Leuven Fac Psychol & Educ Sci Campus Kulak Etienne Sabbelaan 53 B-8500 Kortrijk Belgium|Katholieke Univ Leuven ITEC Imec Leuven Belgium;

    Katholieke Univ Leuven Fac Psychol & Educ Sci Campus Kulak Etienne Sabbelaan 53 B-8500 Kortrijk Belgium|Katholieke Univ Leuven ITEC Imec Leuven Belgium;

    Katholieke Univ Leuven Fac Psychol & Educ Sci Campus Kulak Etienne Sabbelaan 53 B-8500 Kortrijk Belgium|Katholieke Univ Leuven ITEC Imec Leuven Belgium;

    Katholieke Univ Leuven Dept Publ Hlth & Primary Care Fac Med Campus Kulak Etienne Sabbelaan 53 B-8500 Kortrijk Belgium|Katholieke Univ Leuven ITEC Imec Leuven Belgium;

    Katholieke Univ Leuven Fac Psychol & Educ Sci Campus Kulak Etienne Sabbelaan 53 B-8500 Kortrijk Belgium|Katholieke Univ Leuven ITEC Imec Leuven Belgium;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Item response theory; Decision tree learning; Machine learning; Adaptive learning system; Cold-start problem;

    机译：项目响应理论;决策树学习;机器学习;自适应学习系统;冷启动问题;
  • 入库时间 2022-08-18 21:30:24

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